Simulating a continous distribution

In summary, to generate random values according to a specific distribution function, we can use the cumulative distribution function to solve for x. This will result in x being distributed according to the original function. However, when using a weibull distribution, the formula for F(x) may not yield real solutions for certain values of Y. This could be due to an incorrect formula for F(x).
  • #1
MechatronO
30
1
Say we want a set of random values that are distributed according to some distribution function f(x).

A common way to accomplish that is to find the cumulative distribution function F(x) for the distribution and then solve for x according to

F(x) = Y

x = F'(Y)

Then x will be distributed with the original distribution function, if F'(Y) is fed with random values Y ranging from 0-1.

I'm currently trying to do that with a weibull distribution

f(x) = a*b*xb-1*e-a*b*x^b

where F(x) should be

F(x) = e-a - e-a*x^b

when solving for x in F(x) I however get

x = ( - ln(e-a - Y)/a)1/b

When Y> e-a there are no real solutions. Is there a way to get around this? Have I done something wrong?
 
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  • #2
Your F(x) can't be right. F(∞) should be 1.
 

1. What is a continuous distribution?

A continuous distribution is a statistical concept that describes the probability of a continuous random variable taking on a certain value within a given range. It is used to model real-world phenomena that have an infinite number of possible outcomes, such as height or weight.

2. How do you simulate a continuous distribution?

Simulating a continuous distribution involves using mathematical algorithms to generate random numbers that follow the desired distribution. This can be done using software programs such as R or Python, which have built-in functions for generating random numbers from various distributions.

3. What is the purpose of simulating a continuous distribution?

The purpose of simulating a continuous distribution is to understand the behavior of a real-world phenomenon and make predictions about future outcomes. It is also useful for testing statistical hypotheses and evaluating the performance of statistical models.

4. What are some common types of continuous distributions?

Some common types of continuous distributions include the normal distribution, exponential distribution, and uniform distribution. These distributions are often used to model natural phenomena and are characterized by different parameters that determine their shape and behavior.

5. What are the limitations of simulating a continuous distribution?

One limitation of simulating a continuous distribution is that it is based on mathematical approximations and assumptions about the underlying data. This means that the simulated distribution may not perfectly match the real-world phenomenon. Additionally, the accuracy of the simulation depends on the quality and quantity of the data used to build the model.

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